From: Martin Wegmann <wegmann_at_biozentrum.uni-wuerzburg.de>

Date: Mon 26 Sep 2005 - 20:09:36 EST

Number of terminal nodes: 5

Residual mean deviance: 4.714 = 150.8 / 32 Misclassification error rate: 0.8649 = 32 / 37

Date: Mon 26 Sep 2005 - 20:09:36 EST

Hello Robert,

I tried over the week-end and managed, thanks to your help, to get my whole 0/1 matrix into the model. However when I call summary(tree1) I get:

*> summary(tree1)
*

Classification tree:

tree(formula = factor(dat$loc) ~ sp0.m)

Variables actually used in tree construction:

[1] "sp0.m.Pni" "sp0.m.Bbe" [3] "sp0.m.Arid" "sp0.m.Pca"

Number of terminal nodes: 5

Residual mean deviance: 4.714 = 150.8 / 32 Misclassification error rate: 0.8649 = 32 / 37

The plot() output looks reasonable considering that only 5 locations are plotted.

However I have 400 variabels (in the 0/1 matrix sp0.m) and 50 locations (data$loc) and not just 5 terminal nodes. Is there a way to force tree() to plot all locations and their respective variables in the tree construction?

regards, Martin

On Friday 23 September 2005 19:19, Dave Roberts wrote:

> Martin,

*>
**> Sorry, I don't think I read your message carefully enough.
**>
**> When you say the error message is "+", that woudl seem to indicate
**> that you still had an unclosed parenthesis and that the function was
**> looking for more input.
**>
**> Using a smaller data set (160 samples, 169 rows, only 5 classes) it
**> did work fine for me. pa = presence/absence dataframe, opt.5$clustering
**> = cluster IDs.
**>
**> *********************************************************************
**>
**> > test <- tree(factor(opt.5$clustering)~pa)
**> > test
**>
**> node), split, n, deviance, yval, (yprob)
**> * denotes terminal node
**>
**> 1) root 160 371.000 3 ( 0.23750 0.08750 0.57500 0.07500 0.02500 )
**> 2) pa.symore < 0.5 79 216.500 1 ( 0.48101 0.17722 0.15190 0.13924
**> 0.05063 )
**> 4) pa.artarb < 0.5 42 123.600 2 ( 0.07143 0.33333 0.26190 0.23810
**> 0.09524 )
**> 8) pa.macgri < 0.5 31 75.280 2 ( 0.09677 0.45161 0.00000
**> 0.32258 0.12903 )
**> . . .
**> . . .
**> . . .
**> 3) pa.symore > 0.5 81 10.780 3 ( 0.00000 0.00000 0.98765 0.01235
**> 0.00000 )
**> 6) pa.carrss < 0.5 11 6.702 3 ( 0.00000 0.00000 0.90909 0.09091
**> 0.00000 ) *
**> 7) pa.carrss > 0.5 70 0.000 3 ( 0.00000 0.00000 1.00000 0.00000
**> 0.00000 ) *
**>
**> ************************************************************************
**>
**> I'll try agin with a larger dataset and see if it's a memory limitation.
**>
**> Dave Roberts
**>
**> Martin Wegmann wrote:
**> > On Friday 23 September 2005 17:08, Dave Roberts wrote:
**> >>Martin,
**> >>
**> >> If the data are actually coded 0/1, the tree function would
**> >>probably intepret them as integers and try a regression instead of a
**> >>classification. If the dependent variable is called "var", try
**> >
**> > thanks, but I think I provided too less informations.
**> > My dependent variable are the locations which are names (I could
**> > transform them to numbers from 1 - n). The independent variables consist
**> > of 0/1 data (species).
**> > If I do
**> > tree(locations~factor(species1)+factor(species2)+.....+factor(speciesn),
**> > sp_data)
**> > I receive the same results as without the factor() part.
**> > BTW just a subset of the locations are displayed what is pretty weird
**> > considering that I included all locations in the analysis.
**> >
**> > Martin
**> >
**> >>x <- tree(factor(var)~species)
**> >>
**> >>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**> >>David W. Roberts office 406-994-4548
**> >>Professor and Head FAX 406-994-3190
**> >>Department of Ecology email droberts@montana.edu
**> >>Montana State University
**> >>Bozeman, MT 59717-3460
**> >>
**> >>Martin Wegmann wrote:
**> >>>Dear R-user,
**> >>>
**> >>>I tried to generate classification / regression tree with a
**> >>>absence/presence matrix of species (400) in different locations (50) to
**> >>>visualise species which are important for splitting up two locations.
**> >>>Rpart and tree did not work for more than 10 species which is logical
**> >>> due to the limited amount of locations (n=50). However the error prompt
**> >>> is a "+" and no specific message, but I am pretty sure that I did not
**> >>> enter a false sign by mistake.
**> >>>Is it allowed at all to use 0/1 data for this statistical technique and
**> >>>if yes is there a way or different method to use all 400 species
**> >>> entries? Otherwise I would apply a PCA beforehand but I would prefer to
**> >>> have the raw species informations.
**> >>>
**> >>>using R 2.1.1-1 (debian repos.)
**> >>>
**> >>>regards, Martin
**> >>
**> >>______________________________________________
**> >>R-help@stat.math.ethz.ch mailing list
**> >>https://stat.ethz.ch/mailman/listinfo/r-help
**> >>PLEASE do read the posting guide!
**> >>http://www.R-project.org/posting-guide.html
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide!
**> http://www.R-project.org/posting-guide.html
*

-- Martin Wegmann DLR - German Aerospace Center German Remote Sensing Data Center @ Dept.of Geography Remote Sensing and Biodiversity Unit && Dept. of Animal Ecology and Tropical Biology University of Wuerzburg Am Hubland 97074 Würzburg phone: +49-(0)931 - 888 4797 mobile: +49-(0)175 2091725 fax: +49-(0)931 - 888 4961 http://www.biota-africa.org http://www.biogis.de ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Mon Sep 26 20:28:43 2005

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